title: Omics and literature mining.
abstract: The measurement of the simultaneous expression values of thousands of genes or proteins from high throughput Omics platforms creates a large amount of data whose interpretation by inspection can be a daunting task. A major challenge of using such data is to translate these lists of genes/proteins into a better understanding of the underlying biological phenomena. We describe approaches to identify biological concepts in the form of Medical Subject Headings (MeSH terms) as extracted from MEDLINE that are significantly overrepresented within the identified gene set relative to those associated with the overall collection of genes on the underlying Omics platform. The method's principle strength is its ability to simultaneously depict similarities that may exist at the level of biological structure, molecular function, physiology, genetics, and clinically manifest diseases, just as a single published article about a gene of interest may report findings within several of these same dimensions.
authors.affiliation: Computational Biology, Quantitative Sciences, GlaxoSmithKline, King of Prussia, PA, USA. Vinod.D.Kumar@gsk.com
authors.forename: Vinod
authors.initials: V
authors.lastname: Kumar
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D004032:Diet / Q000009:adverse effects; D055808:Drug Discovery; D006801:Humans; D008093:Literature, Modern; D046650:Medical Subject Headings; D009765:Obesity / Q000188:drug therapy / Q000209:etiology / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D010506:Periodicals as Topic; D051381:Rats; D012107:Research Design; D016159:Tumor Suppressor Protein p53 / Q000378:metabolism
chemical_list: D016159:Tumor Suppressor Protein p53